The 1980 stratospheric-tropospheric exchange experiment

Data are presented from the Stratospheric-Tropospheric Water Vapor Exchange Experiment. Measurements were made during 11 flights of the NASA U-2 aircraft which provided data from horizontal traverser and samplings in and about the tops of extensive cirrus-anvil clouds produced by overshooting cumulus turrets. Aircraft measurements were made of water vapor, ozone, ambient and cloud top temperature, fluorocarbons, nitrous oxide, nitric acid, aerosols, and ice crystal populations. Balloonsondes were flown about twice daily providing data on ozone, wind fields, pressure and temperature to altitudes near 30 km. Satellite photography provided detailed cloud and cloud top temperature information. Descriptions of individual experiments and detailed compilations of all results are provided

Facial expression recognition and intensity estimation.

Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis

08191 Working Group Report -- Edge Thresholding

When working with very large networks it is typical for scientists to present a ``thinned out\u27\u27 version of the network in order to avoid the clutter of the entire network. For example in the hypothetical case of illustrating trading patterns between groups of nations it might be appropriate to limit the inclusion of inter-nation edges to all those that are significant in terms of their weight but do not, say, associate with a country outside the grouping. Arising from a discussion during one of the introductory sessions we became interested in a problem relating to the discovery of ``key events\u27\u27 in a network, in terms of an ordered addition of edges to the network